GTM Engineering

Deal Sourcing as a Self-Driving CRM

Opulent finds, scores, and moves acquisition targets through the pipeline while the CRM stays the system of record, with verification gates and one-step handoff.

IntegrationsPlaybooks

AuthorOpulent

CategoryGTM Engineering

FeaturesIntegrations, Playbooks

start it with one message

Source acquisition targets that match our thesis and keep HubSpot the system of record. Search for companies that fit, score each on fit with cited evidence, dedup against the CRM, create or update the record with the score and sources, and hand me a completion packet of what changed and what needs my judgment.

Run this in OpulentCopy it, swap the names for your own, and send it.

connected systems

ApolloFind, enrich, and engage B2B prospects with Apollo

HubSpotSearch CRM data, track contacts, and analyze sales and marketing insights

AttioManage CRM records, deals, tasks, notes, and meetings with Attio

ClayFind, enrich, and research contacts and companies

step 1

Connect your sourcing stack and write a scoring playbook

Connect the tools that find and enrich companies, Apollo and Clay, and the CRM that stays the system of record: HubSpot or Attio. Opulent sources into the first two and writes only verified records into the CRM.

The judgment lives in a playbook, a reusable, named set of steps Opulent follows every run. It defines what a fit looks like for your thesis, what evidence counts, and the score bands, so every target is graded the same way.

Playbook: !source-targets

For each candidate company:
1. Confirm it fits the thesis: [sector], [size], [geo], [signal].
2. Gather evidence from Apollo and Clay: headcount, funding,
   hiring velocity, product signals. Cite the source per fact.
3. Score fit 0-100 with a one-line reason for the band.
4. Check HubSpot for an existing record before creating one.
5. Only write companies scoring 60+. Attach the evidence and
   score to the record; flag anything below the bar for review.

Tip

Point Opulent at a handful of deals you already closed as reference examples. It infers the fit pattern from real winners instead of a description you write from memory.

step 2

Put sourcing on a schedule

Deal sourcing is self-driving when nobody has to start it. Create a schedule, a recurring run on a fixed cadence, and point it at the !source-targets playbook so targets arrive before you sit down.

Click path: Settings, then Schedules, then Create schedule. Name it, set the frequency to daily, choose the !source-targets playbook, and pick a Slack channel for the completion summary.

The sharp edge: a broad search floods the CRM with thin records. Keep the fit filter tight and the write threshold high on the first runs, then widen the search only once the scores read true.

step 3

Watch one target move through the pipeline

Take a candidate the morning run surfaces, a logistics-software company that just posted six senior engineering roles:

Opulent works it end to end and lands the result in the CRM, not a spreadsheet no one opens.

Candidate: Freightwave Systems (freight TMS, ~140 staff)

Run actions:
- Matched thesis: B2B logistics SaaS, 100-250 staff, US. Fit.
- Evidence (Apollo, Clay): 6 senior eng roles opened in 30 days,
  $22M Series B eight months ago, 3 enterprise logos on the site.
- Scored 78/100: "strong hiring + fresh funding, thin on named
  revenue".
- Checked HubSpot: no existing record, no duplicate domain.
- Created the company, attached the score and sources, and added a
  next task: "Confirm ICP before outreach".

step 4

What the run leaves in your CRM

After the run, your CRM holds new work you can act on, each item verifiable against the evidence attached:

Scored company records, created or updated in HubSpot, each carrying its fit score, the evidence behind it, and a source link per claim. A next task on every record that cleared the bar. A completion packet summarizing what changed and which records need your judgment.

Because Opulent verifies the CRM after it writes, re-reading the record it just created, the packet reflects the real state of the pipeline, not an assumption that the write succeeded.

step 5

Sharpen the sourcing loop

When the scores drift from your own judgment (over-rating funding, under-rating a signal you care about) correct the bands in the !source-targets playbook, or write the rule into memory (the notes a run recalls next time) as "weight hiring velocity above headline funding".

Feed outcomes back: when a sourced target closes or dies, tell Opulent, so memory learns which signals actually predicted a deal.

The natural chain: once sourcing is trustworthy, hand the scored accounts to Self-Evolving Pipeline Operations so the same records get weekly review and forecast hygiene without a second setup.